Overview

Dataset statistics

Number of variables70
Number of observations10
Missing cells100
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory572.8 B

Variable types

Numeric32
Categorical28
Unsupported10

Alerts

columna_3 has constant value "2022-06-30" Constant
columna_4 has constant value "1" Constant
columna_14 has constant value "0" Constant
columna_16 has constant value "0" Constant
columna_17 has constant value "0" Constant
columna_18 has constant value "0" Constant
columna_19 has constant value "0" Constant
columna_20 has constant value "0" Constant
columna_22 has constant value "0" Constant
columna_23 has constant value "0" Constant
columna_40 has constant value "C" Constant
columna_50 has constant value "5" Constant
columna_60 has constant value "1" Constant
columna_0 is highly correlated with columna_6 and 3 other fieldsHigh correlation
columna_2 is highly correlated with columna_10 and 4 other fieldsHigh correlation
columna_5 is highly correlated with columna_7 and 16 other fieldsHigh correlation
columna_6 is highly correlated with columna_0 and 3 other fieldsHigh correlation
columna_7 is highly correlated with columna_5 and 17 other fieldsHigh correlation
columna_8 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_9 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_10 is highly correlated with columna_2 and 19 other fieldsHigh correlation
columna_11 is highly correlated with columna_28 and 4 other fieldsHigh correlation
columna_12 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_13 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_15 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_24 is highly correlated with columna_0 and 4 other fieldsHigh correlation
columna_25 is highly correlated with columna_5 and 17 other fieldsHigh correlation
columna_26 is highly correlated with columna_6 and 3 other fieldsHigh correlation
columna_27 is highly correlated with columna_2 and 20 other fieldsHigh correlation
columna_28 is highly correlated with columna_11 and 3 other fieldsHigh correlation
columna_29 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_30 is highly correlated with columna_11 and 4 other fieldsHigh correlation
columna_31 is highly correlated with columna_11 and 4 other fieldsHigh correlation
columna_32 is highly correlated with columna_35 and 2 other fieldsHigh correlation
columna_35 is highly correlated with columna_32 and 3 other fieldsHigh correlation
columna_38 is highly correlated with columna_0High correlation
columna_39 is highly correlated with columna_32 and 3 other fieldsHigh correlation
columna_41 is highly correlated with columna_24 and 3 other fieldsHigh correlation
columna_42 is highly correlated with columna_2 and 4 other fieldsHigh correlation
columna_43 is highly correlated with columna_2 and 4 other fieldsHigh correlation
columna_51 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_55 is highly correlated with columna_0 and 3 other fieldsHigh correlation
columna_56 is highly correlated with columna_7 and 6 other fieldsHigh correlation
columna_61 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_62 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_63 is highly correlated with columna_32 and 2 other fieldsHigh correlation
columna_64 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_65 is highly correlated with columna_11 and 4 other fieldsHigh correlation
columna_66 is highly correlated with columna_2 and 19 other fieldsHigh correlation
columna_67 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_68 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_0 is highly correlated with columna_38High correlation
columna_2 is highly correlated with columna_11 and 6 other fieldsHigh correlation
columna_5 is highly correlated with columna_7 and 17 other fieldsHigh correlation
columna_6 is highly correlated with columna_24 and 3 other fieldsHigh correlation
columna_7 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_8 is highly correlated with columna_5 and 17 other fieldsHigh correlation
columna_9 is highly correlated with columna_5 and 17 other fieldsHigh correlation
columna_10 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_11 is highly correlated with columna_2 and 6 other fieldsHigh correlation
columna_12 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_13 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_15 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_24 is highly correlated with columna_6 and 6 other fieldsHigh correlation
columna_25 is highly correlated with columna_5 and 17 other fieldsHigh correlation
columna_26 is highly correlated with columna_6 and 6 other fieldsHigh correlation
columna_27 is highly correlated with columna_5 and 19 other fieldsHigh correlation
columna_28 is highly correlated with columna_6 and 4 other fieldsHigh correlation
columna_29 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_30 is highly correlated with columna_11 and 2 other fieldsHigh correlation
columna_31 is highly correlated with columna_2 and 6 other fieldsHigh correlation
columna_32 is highly correlated with columna_2 and 4 other fieldsHigh correlation
columna_35 is highly correlated with columna_24 and 3 other fieldsHigh correlation
columna_38 is highly correlated with columna_0High correlation
columna_39 is highly correlated with columna_24 and 3 other fieldsHigh correlation
columna_41 is highly correlated with columna_24 and 5 other fieldsHigh correlation
columna_42 is highly correlated with columna_2 and 6 other fieldsHigh correlation
columna_43 is highly correlated with columna_2 and 6 other fieldsHigh correlation
columna_51 is highly correlated with columna_5 and 17 other fieldsHigh correlation
columna_55 is highly correlated with columna_6 and 3 other fieldsHigh correlation
columna_56 is highly correlated with columna_5 and 6 other fieldsHigh correlation
columna_61 is highly correlated with columna_5 and 17 other fieldsHigh correlation
columna_62 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_63 is highly correlated with columna_2 and 4 other fieldsHigh correlation
columna_64 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_65 is highly correlated with columna_2 and 6 other fieldsHigh correlation
columna_66 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_67 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_68 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_0 is highly correlated with columna_6 and 1 other fieldsHigh correlation
columna_2 is highly correlated with columna_42 and 1 other fieldsHigh correlation
columna_5 is highly correlated with columna_7 and 16 other fieldsHigh correlation
columna_6 is highly correlated with columna_0 and 3 other fieldsHigh correlation
columna_7 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_8 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_9 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_10 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_11 is highly correlated with columna_28 and 3 other fieldsHigh correlation
columna_12 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_13 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_15 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_24 is highly correlated with columna_6 and 3 other fieldsHigh correlation
columna_25 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_26 is highly correlated with columna_6 and 3 other fieldsHigh correlation
columna_27 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_28 is highly correlated with columna_11 and 3 other fieldsHigh correlation
columna_29 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_30 is highly correlated with columna_11 and 4 other fieldsHigh correlation
columna_31 is highly correlated with columna_11 and 3 other fieldsHigh correlation
columna_32 is highly correlated with columna_63High correlation
columna_35 is highly correlated with columna_39 and 1 other fieldsHigh correlation
columna_39 is highly correlated with columna_35 and 1 other fieldsHigh correlation
columna_41 is highly correlated with columna_24 and 3 other fieldsHigh correlation
columna_42 is highly correlated with columna_2 and 1 other fieldsHigh correlation
columna_43 is highly correlated with columna_2 and 1 other fieldsHigh correlation
columna_51 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_55 is highly correlated with columna_0 and 3 other fieldsHigh correlation
columna_56 is highly correlated with columna_30High correlation
columna_61 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_62 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_63 is highly correlated with columna_32High correlation
columna_64 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_65 is highly correlated with columna_11 and 3 other fieldsHigh correlation
columna_66 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_67 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_68 is highly correlated with columna_5 and 16 other fieldsHigh correlation
columna_20 is highly correlated with columna_31 and 26 other fieldsHigh correlation
columna_31 is highly correlated with columna_20 and 18 other fieldsHigh correlation
columna_41 is highly correlated with columna_20 and 17 other fieldsHigh correlation
columna_1 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_19 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_18 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_30 is highly correlated with columna_20 and 18 other fieldsHigh correlation
columna_11 is highly correlated with columna_20 and 18 other fieldsHigh correlation
columna_52 is highly correlated with columna_20 and 17 other fieldsHigh correlation
columna_17 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_38 is highly correlated with columna_20 and 17 other fieldsHigh correlation
columna_3 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_40 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_22 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_48 is highly correlated with columna_20 and 17 other fieldsHigh correlation
columna_23 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_14 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_60 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_37 is highly correlated with columna_20 and 17 other fieldsHigh correlation
columna_36 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_34 is highly correlated with columna_20 and 18 other fieldsHigh correlation
columna_46 is highly correlated with columna_20 and 17 other fieldsHigh correlation
columna_33 is highly correlated with columna_20 and 17 other fieldsHigh correlation
columna_16 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_50 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_4 is highly correlated with columna_20 and 26 other fieldsHigh correlation
columna_44 is highly correlated with columna_20 and 17 other fieldsHigh correlation
columna_65 is highly correlated with columna_20 and 18 other fieldsHigh correlation
columna_0 is highly correlated with columna_1 and 10 other fieldsHigh correlation
columna_1 is highly correlated with columna_0 and 45 other fieldsHigh correlation
columna_2 is highly correlated with columna_1 and 9 other fieldsHigh correlation
columna_5 is highly correlated with columna_1 and 27 other fieldsHigh correlation
columna_6 is highly correlated with columna_1 and 22 other fieldsHigh correlation
columna_7 is highly correlated with columna_1 and 32 other fieldsHigh correlation
columna_8 is highly correlated with columna_1 and 35 other fieldsHigh correlation
columna_9 is highly correlated with columna_1 and 27 other fieldsHigh correlation
columna_10 is highly correlated with columna_1 and 33 other fieldsHigh correlation
columna_11 is highly correlated with columna_0 and 14 other fieldsHigh correlation
columna_12 is highly correlated with columna_1 and 33 other fieldsHigh correlation
columna_13 is highly correlated with columna_0 and 27 other fieldsHigh correlation
columna_15 is highly correlated with columna_0 and 27 other fieldsHigh correlation
columna_24 is highly correlated with columna_1 and 23 other fieldsHigh correlation
columna_25 is highly correlated with columna_1 and 33 other fieldsHigh correlation
columna_26 is highly correlated with columna_1 and 26 other fieldsHigh correlation
columna_27 is highly correlated with columna_1 and 34 other fieldsHigh correlation
columna_28 is highly correlated with columna_1 and 29 other fieldsHigh correlation
columna_29 is highly correlated with columna_1 and 33 other fieldsHigh correlation
columna_30 is highly correlated with columna_0 and 15 other fieldsHigh correlation
columna_31 is highly correlated with columna_0 and 14 other fieldsHigh correlation
columna_32 is highly correlated with columna_1 and 30 other fieldsHigh correlation
columna_33 is highly correlated with columna_1 and 31 other fieldsHigh correlation
columna_34 is highly correlated with columna_1 and 35 other fieldsHigh correlation
columna_35 is highly correlated with columna_1 and 31 other fieldsHigh correlation
columna_36 is highly correlated with columna_0 and 45 other fieldsHigh correlation
columna_37 is highly correlated with columna_1 and 28 other fieldsHigh correlation
columna_38 is highly correlated with columna_1 and 10 other fieldsHigh correlation
columna_39 is highly correlated with columna_1 and 31 other fieldsHigh correlation
columna_41 is highly correlated with columna_1 and 25 other fieldsHigh correlation
columna_42 is highly correlated with columna_1 and 9 other fieldsHigh correlation
columna_43 is highly correlated with columna_1 and 9 other fieldsHigh correlation
columna_44 is highly correlated with columna_1 and 32 other fieldsHigh correlation
columna_46 is highly correlated with columna_1 and 26 other fieldsHigh correlation
columna_48 is highly correlated with columna_1 and 25 other fieldsHigh correlation
columna_51 is highly correlated with columna_1 and 35 other fieldsHigh correlation
columna_52 is highly correlated with columna_1 and 31 other fieldsHigh correlation
columna_55 is highly correlated with columna_1 and 23 other fieldsHigh correlation
columna_56 is highly correlated with columna_1 and 17 other fieldsHigh correlation
columna_61 is highly correlated with columna_1 and 27 other fieldsHigh correlation
columna_62 is highly correlated with columna_0 and 27 other fieldsHigh correlation
columna_63 is highly correlated with columna_1 and 30 other fieldsHigh correlation
columna_64 is highly correlated with columna_0 and 27 other fieldsHigh correlation
columna_65 is highly correlated with columna_0 and 14 other fieldsHigh correlation
columna_66 is highly correlated with columna_1 and 33 other fieldsHigh correlation
columna_67 is highly correlated with columna_1 and 33 other fieldsHigh correlation
columna_68 is highly correlated with columna_0 and 27 other fieldsHigh correlation
columna_21 has 10 (100.0%) missing values Missing
columna_45 has 10 (100.0%) missing values Missing
columna_47 has 10 (100.0%) missing values Missing
columna_49 has 10 (100.0%) missing values Missing
columna_53 has 10 (100.0%) missing values Missing
columna_54 has 10 (100.0%) missing values Missing
columna_57 has 10 (100.0%) missing values Missing
columna_58 has 10 (100.0%) missing values Missing
columna_59 has 10 (100.0%) missing values Missing
columna_69 has 10 (100.0%) missing values Missing
columna_1 is uniformly distributed Uniform
columna_34 is uniformly distributed Uniform
columna_36 is uniformly distributed Uniform
columna_0 has unique values Unique
columna_1 has unique values Unique
columna_2 has unique values Unique
columna_5 has unique values Unique
columna_7 has unique values Unique
columna_8 has unique values Unique
columna_10 has unique values Unique
columna_12 has unique values Unique
columna_25 has unique values Unique
columna_27 has unique values Unique
columna_29 has unique values Unique
columna_36 has unique values Unique
columna_42 has unique values Unique
columna_43 has unique values Unique
columna_51 has unique values Unique
columna_55 has unique values Unique
columna_61 has unique values Unique
columna_66 has unique values Unique
columna_67 has unique values Unique
columna_21 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_45 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_47 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_49 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_53 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_54 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_57 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_58 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_59 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_69 is an unsupported type, check if it needs cleaning or further analysis Unsupported
columna_9 has 2 (20.0%) zeros Zeros
columna_13 has 2 (20.0%) zeros Zeros
columna_15 has 2 (20.0%) zeros Zeros
columna_32 has 2 (20.0%) zeros Zeros
columna_62 has 2 (20.0%) zeros Zeros
columna_63 has 2 (20.0%) zeros Zeros
columna_64 has 2 (20.0%) zeros Zeros
columna_68 has 2 (20.0%) zeros Zeros

Reproduction

Analysis started2022-07-27 15:47:35.576196
Analysis finished2022-07-27 15:49:24.381258
Duration1 minute and 48.81 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

columna_0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4021874855
Minimum4000131899
Maximum4200056194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:24.441854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4000131899
5-th percentile4000421269
Q14000903764
median4002881288
Q34003320528
95-th percentile4111687703
Maximum4200056194
Range199924295
Interquartile range (IQR)2416764.25

Descriptive statistics

Standard deviation62619399.23
Coefficient of variation (CV)0.01556970356
Kurtosis9.988714636
Mean4021874855
Median Absolute Deviation (MAD)1293699.5
Skewness3.159840918
Sum4.021874855 × 1010
Variance3.921189159 × 1015
MonotonicityNot monotonic
2022-07-27T10:49:24.535386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
42000561941
10.0%
40029105551
10.0%
40028520211
10.0%
40036817691
10.0%
40034377741
10.0%
40010943701
10.0%
40007749441
10.0%
40008402291
10.0%
40029687921
10.0%
40001318991
10.0%
ValueCountFrequency (%)
40001318991
10.0%
40007749441
10.0%
40008402291
10.0%
40010943701
10.0%
40028520211
10.0%
40029105551
10.0%
40029687921
10.0%
40034377741
10.0%
40036817691
10.0%
42000561941
10.0%
ValueCountFrequency (%)
42000561941
10.0%
40036817691
10.0%
40034377741
10.0%
40029687921
10.0%
40029105551
10.0%
40028520211
10.0%
40010943701
10.0%
40008402291
10.0%
40007749441
10.0%
40001318991
10.0%

columna_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
174297088
170677080
178847600
173567839
135652652
Other values (5)

Length

Max length9
Median length9
Mean length8.9
Min length8

Characters and Unicode

Total characters89
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row174297088
2nd row170677080
3rd row178847600
4th row173567839
5th row135652652

Common Values

ValueCountFrequency (%)
1742970881
10.0%
1706770801
10.0%
1788476001
10.0%
1735678391
10.0%
1356526521
10.0%
16050028K1
10.0%
969252041
10.0%
2601828541
10.0%
2575009851
10.0%
1776028681
10.0%

Length

2022-07-27T10:49:24.649926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:24.754365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1742970881
10.0%
1706770801
10.0%
1788476001
10.0%
1735678391
10.0%
1356526521
10.0%
16050028k1
10.0%
969252041
10.0%
2601828541
10.0%
2575009851
10.0%
1776028681
10.0%

Most occurring characters

ValueCountFrequency (%)
014
15.7%
712
13.5%
812
13.5%
210
11.2%
610
11.2%
510
11.2%
18
9.0%
95
 
5.6%
44
 
4.5%
33
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number88
98.9%
Uppercase Letter1
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
014
15.9%
712
13.6%
812
13.6%
210
11.4%
610
11.4%
510
11.4%
18
9.1%
95
 
5.7%
44
 
4.5%
33
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
K1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common88
98.9%
Latin1
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
014
15.9%
712
13.6%
812
13.6%
210
11.4%
610
11.4%
510
11.4%
18
9.1%
95
 
5.7%
44
 
4.5%
33
 
3.4%
Latin
ValueCountFrequency (%)
K1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII89
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014
15.7%
712
13.5%
812
13.5%
210
11.2%
610
11.2%
510
11.2%
18
9.0%
95
 
5.6%
44
 
4.5%
33
 
3.4%

columna_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.771116905 × 1010
Minimum1.062035591 × 1010
Maximum1.105217528 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:24.892497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.062035591 × 1010
5-th percentile1.062035592 × 1010
Q11.064679407 × 1010
median5.587142311 × 1010
Q31.008963401 × 1011
95-th percentile1.105210361 × 1011
Maximum1.105217528 × 1011
Range9.990139691 × 1010
Interquartile range (IQR)9.024954598 × 1010

Descriptive statistics

Standard deviation4.967868812 × 1010
Coefficient of variation (CV)0.860815834
Kurtosis-2.534370682
Mean5.771116905 × 1010
Median Absolute Deviation (MAD)4.519980146 × 1010
Skewness0.01814781673
Sum5.771116905 × 1011
Variance2.467972053 × 1021
MonotonicityNot monotonic
2022-07-27T10:49:24.981247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.008217752 × 10111
10.0%
1.008217842 × 10111
10.0%
1.062035594 × 10101
10.0%
1.072127682 × 10101
10.0%
1.00921192 × 10111
10.0%
1.062035591 × 10101
10.0%
1.092107106 × 10101
10.0%
1.105217528 × 10111
10.0%
1.062196649 × 10101
10.0%
1.105201601 × 10111
10.0%
ValueCountFrequency (%)
1.062035591 × 10101
10.0%
1.062035594 × 10101
10.0%
1.062196649 × 10101
10.0%
1.072127682 × 10101
10.0%
1.092107106 × 10101
10.0%
1.008217752 × 10111
10.0%
1.008217842 × 10111
10.0%
1.00921192 × 10111
10.0%
1.105201601 × 10111
10.0%
1.105217528 × 10111
10.0%
ValueCountFrequency (%)
1.105217528 × 10111
10.0%
1.105201601 × 10111
10.0%
1.00921192 × 10111
10.0%
1.008217842 × 10111
10.0%
1.008217752 × 10111
10.0%
1.092107106 × 10101
10.0%
1.072127682 × 10101
10.0%
1.062196649 × 10101
10.0%
1.062035594 × 10101
10.0%
1.062035591 × 10101
10.0%

columna_3
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
2022-06-30
10 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06-30
2nd row2022-06-30
3rd row2022-06-30
4th row2022-06-30
5th row2022-06-30

Common Values

ValueCountFrequency (%)
2022-06-3010
100.0%

Length

2022-07-27T10:49:25.088167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:25.271193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-06-3010
100.0%

Most occurring characters

ValueCountFrequency (%)
230
30.0%
030
30.0%
-20
20.0%
610
 
10.0%
310
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80
80.0%
Dash Punctuation20
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
230
37.5%
030
37.5%
610
 
12.5%
310
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
230
30.0%
030
30.0%
-20
20.0%
610
 
10.0%
310
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
230
30.0%
030
30.0%
-20
20.0%
610
 
10.0%
310
 
10.0%

columna_4
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
1
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110
100.0%

Length

2022-07-27T10:49:25.354014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:25.441030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
110
100.0%

Most occurring characters

ValueCountFrequency (%)
110
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110
100.0%

columna_5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583585.9
Minimum16842
Maximum2804762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:25.513718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16842
5-th percentile23393.55
Q1116040
median286570
Q3662148
95-th percentile1918661.6
Maximum2804762
Range2787920
Interquartile range (IQR)546108

Descriptive statistics

Standard deviation830911.4001
Coefficient of variation (CV)1.423803077
Kurtosis6.942921796
Mean583585.9
Median Absolute Deviation (MAD)262448.5
Skewness2.51517988
Sum5835859
Variance6.904137548 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:25.594279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2895701
10.0%
6732241
10.0%
1758001
10.0%
168421
10.0%
6289201
10.0%
28047621
10.0%
2835701
10.0%
961201
10.0%
8356501
10.0%
314011
10.0%
ValueCountFrequency (%)
168421
10.0%
314011
10.0%
961201
10.0%
1758001
10.0%
2835701
10.0%
2895701
10.0%
6289201
10.0%
6732241
10.0%
8356501
10.0%
28047621
10.0%
ValueCountFrequency (%)
28047621
10.0%
8356501
10.0%
6732241
10.0%
6289201
10.0%
2895701
10.0%
2835701
10.0%
1758001
10.0%
961201
10.0%
314011
10.0%
168421
10.0%

columna_6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.6
Minimum10
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:25.675028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q112
median21
Q331.5
95-th percentile36
Maximum36
Range26
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation10.7827022
Coefficient of variation (CV)0.4991991758
Kurtosis-1.706397219
Mean21.6
Median Absolute Deviation (MAD)10
Skewness0.3288481398
Sum216
Variance116.2666667
MonotonicityNot monotonic
2022-07-27T10:49:25.762869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
102
20.0%
242
20.0%
122
20.0%
362
20.0%
341
10.0%
181
10.0%
ValueCountFrequency (%)
102
20.0%
122
20.0%
181
10.0%
242
20.0%
341
10.0%
362
20.0%
ValueCountFrequency (%)
362
20.0%
341
10.0%
242
20.0%
181
10.0%
122
20.0%
102
20.0%

columna_7
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28472.9
Minimum881
Maximum82493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:25.839578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum881
5-th percentile1116.35
Q13833.75
median25841
Q342058
95-th percentile73672.55
Maximum82493
Range81612
Interquartile range (IQR)38224.25

Descriptive statistics

Standard deviation28000.08235
Coefficient of variation (CV)0.983394117
Kurtosis-0.1552602173
Mean28472.9
Median Absolute Deviation (MAD)21877.5
Skewness0.864314542
Sum284729
Variance784004611.9
MonotonicityNot monotonic
2022-07-27T10:49:25.926319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
289571
10.0%
280511
10.0%
73251
10.0%
14041
10.0%
628921
10.0%
824931
10.0%
236311
10.0%
26701
10.0%
464251
10.0%
8811
10.0%
ValueCountFrequency (%)
8811
10.0%
14041
10.0%
26701
10.0%
73251
10.0%
236311
10.0%
280511
10.0%
289571
10.0%
464251
10.0%
628921
10.0%
824931
10.0%
ValueCountFrequency (%)
824931
10.0%
628921
10.0%
464251
10.0%
289571
10.0%
280511
10.0%
236311
10.0%
73251
10.0%
26701
10.0%
14041
10.0%
8811
10.0%

columna_8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean422564.6
Minimum16842
Maximum1835009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:26.015294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16842
5-th percentile17140.35
Q173976.5
median263265
Q3524499
95-th percentile1297885.85
Maximum1835009
Range1818167
Interquartile range (IQR)450522.5

Descriptive statistics

Standard deviation544192.8453
Coefficient of variation (CV)1.287833494
Kurtosis5.774491608
Mean422564.6
Median Absolute Deviation (MAD)226087.5
Skewness2.250969277
Sum4225646
Variance2.961458529 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:26.089755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2429601
10.0%
4696801
10.0%
1159061
10.0%
168421
10.0%
5427721
10.0%
18350091
10.0%
2835701
10.0%
600001
10.0%
6414021
10.0%
175051
10.0%
ValueCountFrequency (%)
168421
10.0%
175051
10.0%
600001
10.0%
1159061
10.0%
2429601
10.0%
2835701
10.0%
4696801
10.0%
5427721
10.0%
6414021
10.0%
18350091
10.0%
ValueCountFrequency (%)
18350091
10.0%
6414021
10.0%
5427721
10.0%
4696801
10.0%
2835701
10.0%
2429601
10.0%
1159061
10.0%
600001
10.0%
175051
10.0%
168421
10.0%

columna_9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161021.3
Minimum0
Maximum969753
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:26.178310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119452
median53252
Q3167223
95-th percentile624958.95
Maximum969753
Range969753
Interquartile range (IQR)147771

Descriptive statistics

Standard deviation293443.8713
Coefficient of variation (CV)1.822391642
Kurtosis8.321992717
Mean161021.3
Median Absolute Deviation (MAD)46304
Skewness2.817696749
Sum1610213
Variance8.61093056 × 1010
MonotonicityNot monotonic
2022-07-27T10:49:26.282623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
02
20.0%
466101
10.0%
2035441
10.0%
598941
10.0%
861481
10.0%
9697531
10.0%
361201
10.0%
1942481
10.0%
138961
10.0%
ValueCountFrequency (%)
02
20.0%
138961
10.0%
361201
10.0%
466101
10.0%
598941
10.0%
861481
10.0%
1942481
10.0%
2035441
10.0%
9697531
10.0%
ValueCountFrequency (%)
9697531
10.0%
2035441
10.0%
1942481
10.0%
861481
10.0%
598941
10.0%
466101
10.0%
361201
10.0%
138961
10.0%
02
20.0%

columna_10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331200.3
Minimum8895
Maximum1319527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:26.371014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8895
5-th percentile11151.75
Q122370
median229522.5
Q3459906
95-th percentile972582.85
Maximum1319527
Range1310632
Interquartile range (IQR)437536

Descriptive statistics

Standard deviation402939.7884
Coefficient of variation (CV)1.216604539
Kurtosis3.896000773
Mean331200.3
Median Absolute Deviation (MAD)215598
Skewness1.818284702
Sum3312003
Variance1.623604731 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:26.470394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1991061
10.0%
4695701
10.0%
476631
10.0%
139391
10.0%
4309141
10.0%
13195271
10.0%
2599391
10.0%
88951
10.0%
5485401
10.0%
139101
10.0%
ValueCountFrequency (%)
88951
10.0%
139101
10.0%
139391
10.0%
476631
10.0%
1991061
10.0%
2599391
10.0%
4309141
10.0%
4695701
10.0%
5485401
10.0%
13195271
10.0%
ValueCountFrequency (%)
13195271
10.0%
5485401
10.0%
4695701
10.0%
4309141
10.0%
2599391
10.0%
1991061
10.0%
476631
10.0%
139391
10.0%
139101
10.0%
88951
10.0%

columna_11
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
28173
20804
61168
44796

Length

Max length5
Median length1
Mean length2.6
Min length1

Characters and Unicode

Total characters26
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)40.0%

Sample

1st row28173
2nd row0
3rd row20804
4th row0
5th row61168

Common Values

ValueCountFrequency (%)
06
60.0%
281731
 
10.0%
208041
 
10.0%
611681
 
10.0%
447961
 
10.0%

Length

2022-07-27T10:49:26.587448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:26.706267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
06
60.0%
281731
 
10.0%
208041
 
10.0%
611681
 
10.0%
447961
 
10.0%

Most occurring characters

ValueCountFrequency (%)
08
30.8%
83
 
11.5%
13
 
11.5%
43
 
11.5%
63
 
11.5%
22
 
7.7%
72
 
7.7%
31
 
3.8%
91
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08
30.8%
83
 
11.5%
13
 
11.5%
43
 
11.5%
63
 
11.5%
22
 
7.7%
72
 
7.7%
31
 
3.8%
91
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08
30.8%
83
 
11.5%
13
 
11.5%
43
 
11.5%
63
 
11.5%
22
 
7.7%
72
 
7.7%
31
 
3.8%
91
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08
30.8%
83
 
11.5%
13
 
11.5%
43
 
11.5%
63
 
11.5%
22
 
7.7%
72
 
7.7%
31
 
3.8%
91
 
3.8%

columna_12
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346694.4
Minimum13910
Maximum1319527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:26.820869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13910
5-th percentile13923.05
Q157385
median243609
Q3486454
95-th percentile972582.85
Maximum1319527
Range1305617
Interquartile range (IQR)429069

Descriptive statistics

Standard deviation398594.8249
Coefficient of variation (CV)1.149700788
Kurtosis3.755143954
Mean346694.4
Median Absolute Deviation (MAD)227815.5
Skewness1.778873641
Sum3466944
Variance1.588778344 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:26.923507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2272791
10.0%
4695701
10.0%
684671
10.0%
139391
10.0%
4920821
10.0%
13195271
10.0%
2599391
10.0%
536911
10.0%
5485401
10.0%
139101
10.0%
ValueCountFrequency (%)
139101
10.0%
139391
10.0%
536911
10.0%
684671
10.0%
2272791
10.0%
2599391
10.0%
4695701
10.0%
4920821
10.0%
5485401
10.0%
13195271
10.0%
ValueCountFrequency (%)
13195271
10.0%
5485401
10.0%
4920821
10.0%
4695701
10.0%
2599391
10.0%
2272791
10.0%
684671
10.0%
536911
10.0%
139391
10.0%
139101
10.0%

columna_13
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48229.2
Minimum0
Maximum183545
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:27.010922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15014.25
median21957.5
Q380547
95-th percentile139512.5
Maximum183545
Range183545
Interquartile range (IQR)75532.75

Descriptive statistics

Standard deviation58875.67459
Coefficient of variation (CV)1.220747485
Kurtosis2.162133087
Mean48229.2
Median Absolute Deviation (MAD)21957.5
Skewness1.473105151
Sum482292
Variance3466345059
MonotonicityNot monotonic
2022-07-27T10:49:27.085662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
02
20.0%
325501
10.0%
835141
10.0%
109371
10.0%
716461
10.0%
1835451
10.0%
113651
10.0%
856951
10.0%
30401
10.0%
ValueCountFrequency (%)
02
20.0%
30401
10.0%
109371
10.0%
113651
10.0%
325501
10.0%
716461
10.0%
835141
10.0%
856951
10.0%
1835451
10.0%
ValueCountFrequency (%)
1835451
10.0%
856951
10.0%
835141
10.0%
716461
10.0%
325501
10.0%
113651
10.0%
109371
10.0%
30401
10.0%
02
20.0%

columna_14
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2022-07-27T10:49:27.182595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:27.296458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

columna_15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48229.2
Minimum0
Maximum183545
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:27.354903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15014.25
median21957.5
Q380547
95-th percentile139512.5
Maximum183545
Range183545
Interquartile range (IQR)75532.75

Descriptive statistics

Standard deviation58875.67459
Coefficient of variation (CV)1.220747485
Kurtosis2.162133087
Mean48229.2
Median Absolute Deviation (MAD)21957.5
Skewness1.473105151
Sum482292
Variance3466345059
MonotonicityNot monotonic
2022-07-27T10:49:27.437918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
02
20.0%
325501
10.0%
835141
10.0%
109371
10.0%
716461
10.0%
1835451
10.0%
113651
10.0%
856951
10.0%
30401
10.0%
ValueCountFrequency (%)
02
20.0%
30401
10.0%
109371
10.0%
113651
10.0%
325501
10.0%
716461
10.0%
835141
10.0%
856951
10.0%
1835451
10.0%
ValueCountFrequency (%)
1835451
10.0%
856951
10.0%
835141
10.0%
716461
10.0%
325501
10.0%
113651
10.0%
109371
10.0%
30401
10.0%
02
20.0%

columna_16
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2022-07-27T10:49:27.525748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:27.615634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

columna_17
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2022-07-27T10:49:27.675305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:27.858225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

columna_18
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2022-07-27T10:49:27.954688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:28.027886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

columna_19
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2022-07-27T10:49:28.094988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:28.170583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

columna_20
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2022-07-27T10:49:28.247308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:28.343483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

columna_21
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_22
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2022-07-27T10:49:28.405432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:28.481566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

columna_23
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010
100.0%

Length

2022-07-27T10:49:28.548498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:28.625296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
010
100.0%

Most occurring characters

ValueCountFrequency (%)
010
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010
100.0%

columna_24
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.7
Minimum9
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:28.694041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9
Q19
median11.5
Q319
95-th percentile22
Maximum22
Range13
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.677440738
Coefficient of variation (CV)0.4144117327
Kurtosis-1.32589671
Mean13.7
Median Absolute Deviation (MAD)2.5
Skewness0.8290399583
Sum137
Variance32.23333333
MonotonicityNot monotonic
2022-07-27T10:49:28.793149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
94
40.0%
222
20.0%
211
 
10.0%
111
 
10.0%
131
 
10.0%
121
 
10.0%
ValueCountFrequency (%)
94
40.0%
111
 
10.0%
121
 
10.0%
131
 
10.0%
211
 
10.0%
222
20.0%
ValueCountFrequency (%)
222
20.0%
211
 
10.0%
131
 
10.0%
121
 
10.0%
111
 
10.0%
94
40.0%

columna_25
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388708.6
Minimum15444
Maximum1814846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:28.878445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15444
5-th percentile17216.1
Q164488.75
median232569
Q3482978.25
95-th percentile1252877.9
Maximum1814846
Range1799402
Interquartile range (IQR)418489.5

Descriptive statistics

Standard deviation539118.4663
Coefficient of variation (CV)1.386947617
Kurtosis6.503439285
Mean388708.6
Median Absolute Deviation (MAD)205523
Skewness2.429660649
Sum3887086
Variance2.906487207 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:28.980581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2606131
10.0%
2524591
10.0%
1538251
10.0%
154441
10.0%
5660281
10.0%
18148461
10.0%
2126791
10.0%
347101
10.0%
5571001
10.0%
193821
10.0%
ValueCountFrequency (%)
154441
10.0%
193821
10.0%
347101
10.0%
1538251
10.0%
2126791
10.0%
2524591
10.0%
2606131
10.0%
5571001
10.0%
5660281
10.0%
18148461
10.0%
ValueCountFrequency (%)
18148461
10.0%
5660281
10.0%
5571001
10.0%
2606131
10.0%
2524591
10.0%
2126791
10.0%
1538251
10.0%
347101
10.0%
193821
10.0%
154441
10.0%

columna_26
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:29.098675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2.5
Q311
95-th percentile13.55
Maximum14
Range13
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.621387729
Coefficient of variation (CV)1.040997728
Kurtosis-1.321159613
Mean5.4
Median Absolute Deviation (MAD)1.5
Skewness0.8757616478
Sum54
Variance31.6
MonotonicityNot monotonic
2022-07-27T10:49:29.181217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
14
40.0%
132
20.0%
21
 
10.0%
141
 
10.0%
51
 
10.0%
31
 
10.0%
ValueCountFrequency (%)
14
40.0%
21
 
10.0%
31
 
10.0%
51
 
10.0%
132
20.0%
141
 
10.0%
ValueCountFrequency (%)
141
 
10.0%
132
20.0%
51
 
10.0%
31
 
10.0%
21
 
10.0%
14
40.0%

columna_27
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156054.4
Minimum2808
Maximum1154902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:29.283663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2808
5-th percentile6698.25
Q115920.25
median28504
Q387141.75
95-th percentile697869.85
Maximum1154902
Range1152094
Interquartile range (IQR)71221.5

Descriptive statistics

Standard deviation353583.3504
Coefficient of variation (CV)2.265769824
Kurtosis9.592507048
Mean156054.4
Median Absolute Deviation (MAD)21373.5
Skewness3.077511842
Sum1560544
Variance1.250211857 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:29.368800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
289571
10.0%
280511
10.0%
952251
10.0%
28081
10.0%
628921
10.0%
11549021
10.0%
236311
10.0%
133501
10.0%
1392751
10.0%
114531
10.0%
ValueCountFrequency (%)
28081
10.0%
114531
10.0%
133501
10.0%
236311
10.0%
280511
10.0%
289571
10.0%
628921
10.0%
952251
10.0%
1392751
10.0%
11549021
10.0%
ValueCountFrequency (%)
11549021
10.0%
1392751
10.0%
952251
10.0%
628921
10.0%
289571
10.0%
280511
10.0%
236311
10.0%
133501
10.0%
114531
10.0%
28081
10.0%

columna_28
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.4
Minimum8
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:29.461313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q18
median10.5
Q318.75
95-th percentile23
Maximum23
Range15
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation6.363087999
Coefficient of variation (CV)0.4748573134
Kurtosis-1.349220287
Mean13.4
Median Absolute Deviation (MAD)2.5
Skewness0.7429133201
Sum134
Variance40.48888889
MonotonicityNot monotonic
2022-07-27T10:49:29.534397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
84
40.0%
232
20.0%
101
 
10.0%
201
 
10.0%
111
 
10.0%
151
 
10.0%
ValueCountFrequency (%)
84
40.0%
101
 
10.0%
111
 
10.0%
151
 
10.0%
201
 
10.0%
232
20.0%
ValueCountFrequency (%)
232
20.0%
201
 
10.0%
151
 
10.0%
111
 
10.0%
101
 
10.0%
84
40.0%

columna_29
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410040.4
Minimum14040
Maximum1649860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:29.616132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14040
5-th percentile16840.35
Q130670
median245798.5
Q3609663.75
95-th percentile1220791.75
Maximum1649860
Range1635820
Interquartile range (IQR)578993.75

Descriptive statistics

Standard deviation507802.5255
Coefficient of variation (CV)1.238420715
Kurtosis3.735133088
Mean410040.4
Median Absolute Deviation (MAD)228647
Skewness1.814365806
Sum4100404
Variance2.578634049 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:29.721089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2316561
10.0%
6451731
10.0%
586001
10.0%
140401
10.0%
5031361
10.0%
16498601
10.0%
2599411
10.0%
213601
10.0%
6963751
10.0%
202631
10.0%
ValueCountFrequency (%)
140401
10.0%
202631
10.0%
213601
10.0%
586001
10.0%
2316561
10.0%
2599411
10.0%
5031361
10.0%
6451731
10.0%
6963751
10.0%
16498601
10.0%
ValueCountFrequency (%)
16498601
10.0%
6963751
10.0%
6451731
10.0%
5031361
10.0%
2599411
10.0%
2316561
10.0%
586001
10.0%
213601
10.0%
202631
10.0%
140401
10.0%

columna_30
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
1
3
23

Length

Max length2
Median length1
Mean length1.1
Min length1

Characters and Unicode

Total characters11
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st row1
2nd row0
3rd row3
4th row0
5th row1

Common Values

ValueCountFrequency (%)
06
60.0%
12
 
20.0%
31
 
10.0%
231
 
10.0%

Length

2022-07-27T10:49:29.824065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:29.934933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
06
60.0%
12
 
20.0%
31
 
10.0%
231
 
10.0%

Most occurring characters

ValueCountFrequency (%)
06
54.5%
12
 
18.2%
32
 
18.2%
21
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06
54.5%
12
 
18.2%
32
 
18.2%
21
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common11
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06
54.5%
12
 
18.2%
32
 
18.2%
21
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06
54.5%
12
 
18.2%
32
 
18.2%
21
 
9.1%

columna_31
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
28957
21975
62892
61410

Length

Max length5
Median length1
Mean length2.6
Min length1

Characters and Unicode

Total characters26
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)40.0%

Sample

1st row28957
2nd row0
3rd row21975
4th row0
5th row62892

Common Values

ValueCountFrequency (%)
06
60.0%
289571
 
10.0%
219751
 
10.0%
628921
 
10.0%
614101
 
10.0%

Length

2022-07-27T10:49:30.024443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:30.123691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
06
60.0%
289571
 
10.0%
219751
 
10.0%
628921
 
10.0%
614101
 
10.0%

Most occurring characters

ValueCountFrequency (%)
07
26.9%
24
15.4%
93
11.5%
13
11.5%
82
 
7.7%
52
 
7.7%
72
 
7.7%
62
 
7.7%
41
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07
26.9%
24
15.4%
93
11.5%
13
11.5%
82
 
7.7%
52
 
7.7%
72
 
7.7%
62
 
7.7%
41
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07
26.9%
24
15.4%
93
11.5%
13
11.5%
82
 
7.7%
52
 
7.7%
72
 
7.7%
62
 
7.7%
41
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07
26.9%
24
15.4%
93
11.5%
13
11.5%
82
 
7.7%
52
 
7.7%
72
 
7.7%
62
 
7.7%
41
 
3.8%

columna_32
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.193247
Minimum0
Maximum2.96333
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:30.234455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.3416625
median2.78583
Q32.8018725
95-th percentile2.9040785
Maximum2.96333
Range2.96333
Interquartile range (IQR)0.46021

Descriptive statistics

Standard deviation1.173293906
Coefficient of variation (CV)0.534957488
Kurtosis1.122324756
Mean2.193247
Median Absolute Deviation (MAD)0.032915
Skewness-1.662309939
Sum21.93247
Variance1.376618589
MonotonicityNot monotonic
2022-07-27T10:49:30.318611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.792
20.0%
02
20.0%
2.781661
10.0%
2.831661
10.0%
2.198331
10.0%
2.771661
10.0%
2.805831
10.0%
2.963331
10.0%
ValueCountFrequency (%)
02
20.0%
2.198331
10.0%
2.771661
10.0%
2.781661
10.0%
2.792
20.0%
2.805831
10.0%
2.831661
10.0%
2.963331
10.0%
ValueCountFrequency (%)
2.963331
10.0%
2.831661
10.0%
2.805831
10.0%
2.792
20.0%
2.781661
10.0%
2.771661
10.0%
2.198331
10.0%
02
20.0%

columna_33
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
2021-05-11
2021-10-11
2021-05-10
2021-11-25
2021-01-11

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st row2021-05-11
2nd row2021-10-11
3rd row2021-05-11
4th row2021-05-10
5th row2021-05-11

Common Values

ValueCountFrequency (%)
2021-05-113
30.0%
2021-10-112
20.0%
2021-05-102
20.0%
2021-11-251
 
10.0%
2021-01-111
 
10.0%
2021-10-101
 
10.0%

Length

2022-07-27T10:49:30.518635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:30.637497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-05-113
30.0%
2021-10-112
20.0%
2021-05-102
20.0%
2021-11-251
 
10.0%
2021-01-111
 
10.0%
2021-10-101
 
10.0%

Most occurring characters

ValueCountFrequency (%)
131
31.0%
022
22.0%
221
21.0%
-20
20.0%
56
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80
80.0%
Dash Punctuation20
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131
38.8%
022
27.5%
221
26.2%
56
 
7.5%
Dash Punctuation
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
131
31.0%
022
22.0%
221
21.0%
-20
20.0%
56
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131
31.0%
022
22.0%
221
21.0%
-20
20.0%
56
 
6.0%

columna_34
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
2021-05-10
2021-10-10
2020-05-10
2021-05-08
2020-09-25
Other values (4)

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)80.0%

Sample

1st row2021-05-10
2nd row2021-10-10
3rd row2020-05-10
4th row2021-05-08
5th row2021-05-10

Common Values

ValueCountFrequency (%)
2021-05-102
20.0%
2021-10-101
10.0%
2020-05-101
10.0%
2021-05-081
10.0%
2020-09-251
10.0%
2021-01-101
10.0%
2021-10-061
10.0%
2021-10-071
10.0%
2020-05-091
10.0%

Length

2022-07-27T10:49:30.735797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:30.860965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-05-102
20.0%
2021-10-101
10.0%
2020-05-101
10.0%
2021-05-081
10.0%
2020-09-251
10.0%
2021-01-101
10.0%
2021-10-061
10.0%
2021-10-071
10.0%
2020-05-091
10.0%

Most occurring characters

ValueCountFrequency (%)
032
32.0%
221
21.0%
-20
20.0%
116
16.0%
56
 
6.0%
92
 
2.0%
81
 
1.0%
61
 
1.0%
71
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80
80.0%
Dash Punctuation20
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032
40.0%
221
26.2%
116
20.0%
56
 
7.5%
92
 
2.5%
81
 
1.2%
61
 
1.2%
71
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
032
32.0%
221
21.0%
-20
20.0%
116
16.0%
56
 
6.0%
92
 
2.0%
81
 
1.0%
61
 
1.0%
71
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032
32.0%
221
21.0%
-20
20.0%
116
16.0%
56
 
6.0%
92
 
2.0%
81
 
1.0%
61
 
1.0%
71
 
1.0%

columna_35
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.7
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:30.951870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1121.25
median375.5
Q3463
95-th percentile483.35
Maximum500
Range499
Interquartile range (IQR)341.75

Descriptive statistics

Standard deviation204.7747
Coefficient of variation (CV)0.6878558953
Kurtosis-1.53135731
Mean297.7
Median Absolute Deviation (MAD)101.5
Skewness-0.5983219547
Sum2977
Variance41932.67778
MonotonicityNot monotonic
2022-07-27T10:49:31.033257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4634
40.0%
12
20.0%
2601
 
10.0%
751
 
10.0%
5001
 
10.0%
2881
 
10.0%
ValueCountFrequency (%)
12
20.0%
751
 
10.0%
2601
 
10.0%
2881
 
10.0%
4634
40.0%
5001
 
10.0%
ValueCountFrequency (%)
5001
 
10.0%
4634
40.0%
2881
 
10.0%
2601
 
10.0%
751
 
10.0%
12
20.0%

columna_36
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
2021-09-30
2022-01-05
2021-11-10
2021-07-09
2021-07-10
Other values (5)

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row2021-09-30
2nd row2022-01-05
3rd row2021-11-10
4th row2021-07-09
5th row2021-07-10

Common Values

ValueCountFrequency (%)
2021-09-301
10.0%
2022-01-051
10.0%
2021-11-101
10.0%
2021-07-091
10.0%
2021-07-101
10.0%
2021-10-261
10.0%
2021-04-111
10.0%
2021-05-101
10.0%
2021-09-151
10.0%
2021-09-141
10.0%

Length

2022-07-27T10:49:31.108035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:31.237241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-09-301
10.0%
2022-01-051
10.0%
2021-11-101
10.0%
2021-07-091
10.0%
2021-07-101
10.0%
2021-10-261
10.0%
2021-04-111
10.0%
2021-05-101
10.0%
2021-09-151
10.0%
2021-09-141
10.0%

Most occurring characters

ValueCountFrequency (%)
025
25.0%
222
22.0%
120
20.0%
-20
20.0%
94
 
4.0%
53
 
3.0%
72
 
2.0%
42
 
2.0%
31
 
1.0%
61
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80
80.0%
Dash Punctuation20
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025
31.2%
222
27.5%
120
25.0%
94
 
5.0%
53
 
3.8%
72
 
2.5%
42
 
2.5%
31
 
1.2%
61
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025
25.0%
222
22.0%
120
20.0%
-20
20.0%
94
 
4.0%
53
 
3.0%
72
 
2.0%
42
 
2.0%
31
 
1.0%
61
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025
25.0%
222
22.0%
120
20.0%
-20
20.0%
94
 
4.0%
53
 
3.0%
72
 
2.0%
42
 
2.0%
31
 
1.0%
61
 
1.0%

columna_37
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
2021-08-08
2020-01-06
2021-06-29
2021-10-09
2021-08-31
Other values (3)

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)60.0%

Sample

1st row2021-08-08
2nd row2021-08-08
3rd row2020-01-06
4th row2021-06-29
5th row2021-10-09

Common Values

ValueCountFrequency (%)
2021-08-082
20.0%
2020-01-062
20.0%
2021-06-291
10.0%
2021-10-091
10.0%
2021-08-311
10.0%
2021-11-051
10.0%
2021-01-061
10.0%
2020-11-051
10.0%

Length

2022-07-27T10:49:31.352502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:31.467225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-08-082
20.0%
2020-01-062
20.0%
2021-06-291
10.0%
2021-10-091
10.0%
2021-08-311
10.0%
2021-11-051
10.0%
2021-01-061
10.0%
2020-11-051
10.0%

Most occurring characters

ValueCountFrequency (%)
029
29.0%
221
21.0%
-20
20.0%
116
16.0%
85
 
5.0%
64
 
4.0%
92
 
2.0%
52
 
2.0%
31
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80
80.0%
Dash Punctuation20
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029
36.2%
221
26.2%
116
20.0%
85
 
6.2%
64
 
5.0%
92
 
2.5%
52
 
2.5%
31
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029
29.0%
221
21.0%
-20
20.0%
116
16.0%
85
 
5.0%
64
 
4.0%
92
 
2.0%
52
 
2.0%
31
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029
29.0%
221
21.0%
-20
20.0%
116
16.0%
85
 
5.0%
64
 
4.0%
92
 
2.0%
52
 
2.0%
31
 
1.0%

columna_38
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
24101
24141

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters50
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row24141
2nd row24101
3rd row24101
4th row24101
5th row24101

Common Values

ValueCountFrequency (%)
241019
90.0%
241411
 
10.0%

Length

2022-07-27T10:49:31.605882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:31.698651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
241019
90.0%
241411
 
10.0%

Most occurring characters

ValueCountFrequency (%)
120
40.0%
411
22.0%
210
20.0%
09
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
120
40.0%
411
22.0%
210
20.0%
09
18.0%

Most occurring scripts

ValueCountFrequency (%)
Common50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
120
40.0%
411
22.0%
210
20.0%
09
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
40.0%
411
22.0%
210
20.0%
09
18.0%

columna_39
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.7
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:31.755649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1121.25
median375.5
Q3463
95-th percentile483.35
Maximum500
Range499
Interquartile range (IQR)341.75

Descriptive statistics

Standard deviation204.7747
Coefficient of variation (CV)0.6878558953
Kurtosis-1.53135731
Mean297.7
Median Absolute Deviation (MAD)101.5
Skewness-0.5983219547
Sum2977
Variance41932.67778
MonotonicityNot monotonic
2022-07-27T10:49:31.855648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4634
40.0%
12
20.0%
2601
 
10.0%
751
 
10.0%
5001
 
10.0%
2881
 
10.0%
ValueCountFrequency (%)
12
20.0%
751
 
10.0%
2601
 
10.0%
2881
 
10.0%
4634
40.0%
5001
 
10.0%
ValueCountFrequency (%)
5001
 
10.0%
4634
40.0%
2881
 
10.0%
2601
 
10.0%
751
 
10.0%
12
20.0%

columna_40
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
C
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C10
100.0%

Length

2022-07-27T10:49:31.947535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:32.038951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
c10
100.0%

Most occurring characters

ValueCountFrequency (%)
C10
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C10
100.0%

columna_41
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
252
433
432
347
254

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st row433
2nd row433
3rd row252
4th row432
5th row252

Common Values

ValueCountFrequency (%)
2524
40.0%
4332
20.0%
4322
20.0%
3471
 
10.0%
2541
 
10.0%

Length

2022-07-27T10:49:32.119846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:32.228777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2524
40.0%
4332
20.0%
4322
20.0%
3471
 
10.0%
2541
 
10.0%

Most occurring characters

ValueCountFrequency (%)
211
36.7%
37
23.3%
46
20.0%
55
16.7%
71
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
211
36.7%
37
23.3%
46
20.0%
55
16.7%
71
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
211
36.7%
37
23.3%
46
20.0%
55
16.7%
71
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
211
36.7%
37
23.3%
46
20.0%
55
16.7%
71
 
3.3%

columna_42
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.771116905 × 1010
Minimum1.062035591 × 1010
Maximum1.105217528 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:32.340530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.062035591 × 1010
5-th percentile1.062035592 × 1010
Q11.064679407 × 1010
median5.587142311 × 1010
Q31.008963401 × 1011
95-th percentile1.105210361 × 1011
Maximum1.105217528 × 1011
Range9.990139691 × 1010
Interquartile range (IQR)9.024954598 × 1010

Descriptive statistics

Standard deviation4.967868812 × 1010
Coefficient of variation (CV)0.860815834
Kurtosis-2.534370682
Mean5.771116905 × 1010
Median Absolute Deviation (MAD)4.519980146 × 1010
Skewness0.01814781673
Sum5.771116905 × 1011
Variance2.467972053 × 1021
MonotonicityNot monotonic
2022-07-27T10:49:32.434178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.008217752 × 10111
10.0%
1.008217842 × 10111
10.0%
1.062035594 × 10101
10.0%
1.072127682 × 10101
10.0%
1.00921192 × 10111
10.0%
1.062035591 × 10101
10.0%
1.092107106 × 10101
10.0%
1.105217528 × 10111
10.0%
1.062196649 × 10101
10.0%
1.105201601 × 10111
10.0%
ValueCountFrequency (%)
1.062035591 × 10101
10.0%
1.062035594 × 10101
10.0%
1.062196649 × 10101
10.0%
1.072127682 × 10101
10.0%
1.092107106 × 10101
10.0%
1.008217752 × 10111
10.0%
1.008217842 × 10111
10.0%
1.00921192 × 10111
10.0%
1.105201601 × 10111
10.0%
1.105217528 × 10111
10.0%
ValueCountFrequency (%)
1.105217528 × 10111
10.0%
1.105201601 × 10111
10.0%
1.00921192 × 10111
10.0%
1.008217842 × 10111
10.0%
1.008217752 × 10111
10.0%
1.092107106 × 10101
10.0%
1.072127682 × 10101
10.0%
1.062196649 × 10101
10.0%
1.062035594 × 10101
10.0%
1.062035591 × 10101
10.0%

columna_43
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.771116905 × 1010
Minimum1.062035591 × 1010
Maximum1.105217528 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:32.542993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.062035591 × 1010
5-th percentile1.062035592 × 1010
Q11.064679407 × 1010
median5.587142311 × 1010
Q31.008963401 × 1011
95-th percentile1.105210361 × 1011
Maximum1.105217528 × 1011
Range9.990139691 × 1010
Interquartile range (IQR)9.024954598 × 1010

Descriptive statistics

Standard deviation4.967868812 × 1010
Coefficient of variation (CV)0.860815834
Kurtosis-2.534370682
Mean5.771116905 × 1010
Median Absolute Deviation (MAD)4.519980146 × 1010
Skewness0.01814781673
Sum5.771116905 × 1011
Variance2.467972053 × 1021
MonotonicityNot monotonic
2022-07-27T10:49:32.637848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.008217752 × 10111
10.0%
1.008217842 × 10111
10.0%
1.062035594 × 10101
10.0%
1.072127682 × 10101
10.0%
1.00921192 × 10111
10.0%
1.062035591 × 10101
10.0%
1.092107106 × 10101
10.0%
1.105217528 × 10111
10.0%
1.062196649 × 10101
10.0%
1.105201601 × 10111
10.0%
ValueCountFrequency (%)
1.062035591 × 10101
10.0%
1.062035594 × 10101
10.0%
1.062196649 × 10101
10.0%
1.072127682 × 10101
10.0%
1.092107106 × 10101
10.0%
1.008217752 × 10111
10.0%
1.008217842 × 10111
10.0%
1.00921192 × 10111
10.0%
1.105201601 × 10111
10.0%
1.105217528 × 10111
10.0%
ValueCountFrequency (%)
1.105217528 × 10111
10.0%
1.105201601 × 10111
10.0%
1.00921192 × 10111
10.0%
1.008217842 × 10111
10.0%
1.008217752 × 10111
10.0%
1.092107106 × 10101
10.0%
1.072127682 × 10101
10.0%
1.062196649 × 10101
10.0%
1.062035594 × 10101
10.0%
1.062035591 × 10101
10.0%

columna_44
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
2022-07-05
2023-09-10
2022-09-05
2023-06-25
2022-09-01
Other values (3)

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)70.0%

Sample

1st row2022-07-05
2nd row2023-09-10
3rd row2022-09-05
4th row2022-07-05
5th row2022-07-05

Common Values

ValueCountFrequency (%)
2022-07-053
30.0%
2023-09-101
 
10.0%
2022-09-051
 
10.0%
2023-06-251
 
10.0%
2022-09-011
 
10.0%
2024-05-101
 
10.0%
2022-12-101
 
10.0%
2023-08-051
 
10.0%

Length

2022-07-27T10:49:32.732088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:32.847231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-07-053
30.0%
2023-09-101
 
10.0%
2022-09-051
 
10.0%
2023-06-251
 
10.0%
2022-09-011
 
10.0%
2024-05-101
 
10.0%
2022-12-101
 
10.0%
2023-08-051
 
10.0%

Most occurring characters

ValueCountFrequency (%)
228
28.0%
028
28.0%
-20
20.0%
57
 
7.0%
15
 
5.0%
73
 
3.0%
33
 
3.0%
93
 
3.0%
61
 
1.0%
41
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80
80.0%
Dash Punctuation20
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
228
35.0%
028
35.0%
57
 
8.8%
15
 
6.2%
73
 
3.8%
33
 
3.8%
93
 
3.8%
61
 
1.2%
41
 
1.2%
81
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
228
28.0%
028
28.0%
-20
20.0%
57
 
7.0%
15
 
5.0%
73
 
3.0%
33
 
3.0%
93
 
3.0%
61
 
1.0%
41
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
228
28.0%
028
28.0%
-20
20.0%
57
 
7.0%
15
 
5.0%
73
 
3.0%
33
 
3.0%
93
 
3.0%
61
 
1.0%
41
 
1.0%

columna_45
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_46
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
2.- Coas
7.- Refinanciamiento
3.- Avance
5.- Repactado

Length

Max length20
Median length13
Mean length13.5
Min length8

Characters and Unicode

Total characters135
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st row2.- Coas
2nd row2.- Coas
3rd row7.- Refinanciamiento
4th row2.- Coas
5th row7.- Refinanciamiento

Common Values

ValueCountFrequency (%)
2.- Coas4
40.0%
7.- Refinanciamiento4
40.0%
3.- Avance1
 
10.0%
5.- Repactado1
 
10.0%

Length

2022-07-27T10:49:32.951941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:33.054701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
24
20.0%
coas4
20.0%
74
20.0%
refinanciamiento4
20.0%
31
 
5.0%
avance1
 
5.0%
51
 
5.0%
repactado1
 
5.0%

Most occurring characters

ValueCountFrequency (%)
a15
11.1%
n13
 
9.6%
i12
 
8.9%
-10
 
7.4%
10
 
7.4%
e10
 
7.4%
.10
 
7.4%
o9
 
6.7%
c6
 
4.4%
t5
 
3.7%
Other values (13)35
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter85
63.0%
Dash Punctuation10
 
7.4%
Space Separator10
 
7.4%
Other Punctuation10
 
7.4%
Uppercase Letter10
 
7.4%
Decimal Number10
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a15
17.6%
n13
15.3%
i12
14.1%
e10
11.8%
o9
10.6%
c6
 
7.1%
t5
 
5.9%
m4
 
4.7%
f4
 
4.7%
s4
 
4.7%
Other values (3)3
 
3.5%
Decimal Number
ValueCountFrequency (%)
24
40.0%
74
40.0%
31
 
10.0%
51
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
R5
50.0%
C4
40.0%
A1
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
-10
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Punctuation
ValueCountFrequency (%)
.10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin95
70.4%
Common40
29.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a15
15.8%
n13
13.7%
i12
12.6%
e10
10.5%
o9
9.5%
c6
 
6.3%
t5
 
5.3%
R5
 
5.3%
m4
 
4.2%
f4
 
4.2%
Other values (6)12
12.6%
Common
ValueCountFrequency (%)
-10
25.0%
10
25.0%
.10
25.0%
24
 
10.0%
74
 
10.0%
31
 
2.5%
51
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a15
11.1%
n13
 
9.6%
i12
 
8.9%
-10
 
7.4%
10
 
7.4%
e10
 
7.4%
.10
 
7.4%
o9
 
6.7%
c6
 
4.4%
t5
 
3.7%
Other values (13)35
25.9%

columna_47
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_48
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
RENEGOCIACION X X REFINANCIAMIENTO TOTAL
Compra Cuotas Emisor
Compra Cuotas Comercio TRANSBANK
AVANCE_ABCDIN
RENEGOCIACION X REPACTACION

Length

Max length40
Median length32
Mean length30.4
Min length13

Characters and Unicode

Total characters304
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st rowCompra Cuotas Emisor
2nd rowCompra Cuotas Emisor
3rd rowRENEGOCIACION X X REFINANCIAMIENTO TOTAL
4th rowCompra Cuotas Comercio TRANSBANK
5th rowRENEGOCIACION X X REFINANCIAMIENTO TOTAL

Common Values

ValueCountFrequency (%)
RENEGOCIACION X X REFINANCIAMIENTO TOTAL4
40.0%
Compra Cuotas Emisor2
20.0%
Compra Cuotas Comercio TRANSBANK2
20.0%
AVANCE_ABCDIN1
 
10.0%
RENEGOCIACION X REPACTACION1
 
10.0%

Length

2022-07-27T10:49:33.142163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:33.297379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
x9
23.7%
renegociacion5
13.2%
refinanciamiento4
10.5%
total4
10.5%
compra4
10.5%
cuotas4
10.5%
emisor2
 
5.3%
comercio2
 
5.3%
transbank2
 
5.3%
avance_abcdin1
 
2.6%

Most occurring characters

ValueCountFrequency (%)
N29
 
9.5%
C28
 
9.2%
28
 
9.2%
A26
 
8.6%
I24
 
7.9%
E22
 
7.2%
O19
 
6.2%
T15
 
4.9%
o14
 
4.6%
R12
 
3.9%
Other values (22)87
28.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter211
69.4%
Lowercase Letter64
 
21.1%
Space Separator28
 
9.2%
Connector Punctuation1
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N29
13.7%
C28
13.3%
A26
12.3%
I24
11.4%
E22
10.4%
O19
9.0%
T15
7.1%
R12
5.7%
X9
 
4.3%
G5
 
2.4%
Other values (9)22
10.4%
Lowercase Letter
ValueCountFrequency (%)
o14
21.9%
m8
12.5%
r8
12.5%
a8
12.5%
s6
9.4%
i4
 
6.2%
t4
 
6.2%
u4
 
6.2%
p4
 
6.2%
e2
 
3.1%
Space Separator
ValueCountFrequency (%)
28
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin275
90.5%
Common29
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N29
 
10.5%
C28
 
10.2%
A26
 
9.5%
I24
 
8.7%
E22
 
8.0%
O19
 
6.9%
T15
 
5.5%
o14
 
5.1%
R12
 
4.4%
X9
 
3.3%
Other values (20)77
28.0%
Common
ValueCountFrequency (%)
28
96.6%
_1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N29
 
9.5%
C28
 
9.2%
28
 
9.2%
A26
 
8.6%
I24
 
7.9%
E22
 
7.2%
O19
 
6.2%
T15
 
4.9%
o14
 
4.6%
R12
 
3.9%
Other values (22)87
28.6%

columna_49
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_50
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
5
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
510
100.0%

Length

2022-07-27T10:49:33.515550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:33.602367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
510
100.0%

Most occurring characters

ValueCountFrequency (%)
510
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
510
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
510
100.0%

columna_51
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean422564.6
Minimum16842
Maximum1835009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:33.663038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16842
5-th percentile17140.35
Q173976.5
median263265
Q3524499
95-th percentile1297885.85
Maximum1835009
Range1818167
Interquartile range (IQR)450522.5

Descriptive statistics

Standard deviation544192.8453
Coefficient of variation (CV)1.287833494
Kurtosis5.774491608
Mean422564.6
Median Absolute Deviation (MAD)226087.5
Skewness2.250969277
Sum4225646
Variance2.961458529 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:33.765608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2429601
10.0%
4696801
10.0%
1159061
10.0%
168421
10.0%
5427721
10.0%
18350091
10.0%
2835701
10.0%
600001
10.0%
6414021
10.0%
175051
10.0%
ValueCountFrequency (%)
168421
10.0%
175051
10.0%
600001
10.0%
1159061
10.0%
2429601
10.0%
2835701
10.0%
4696801
10.0%
5427721
10.0%
6414021
10.0%
18350091
10.0%
ValueCountFrequency (%)
18350091
10.0%
6414021
10.0%
5427721
10.0%
4696801
10.0%
2835701
10.0%
2429601
10.0%
1159061
10.0%
600001
10.0%
175051
10.0%
168421
10.0%

columna_52
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
2021-11-05
2021-11-10
2021-10-05
2021-11-25
2021-11-01

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st row2021-11-05
2nd row2021-11-10
3rd row2021-11-05
4th row2021-10-05
5th row2021-11-05

Common Values

ValueCountFrequency (%)
2021-11-053
30.0%
2021-11-102
20.0%
2021-10-052
20.0%
2021-11-251
 
10.0%
2021-11-011
 
10.0%
2021-10-101
 
10.0%

Length

2022-07-27T10:49:33.881354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:33.999048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-11-053
30.0%
2021-11-102
20.0%
2021-10-052
20.0%
2021-11-251
 
10.0%
2021-11-011
 
10.0%
2021-10-101
 
10.0%

Most occurring characters

ValueCountFrequency (%)
131
31.0%
022
22.0%
221
21.0%
-20
20.0%
56
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80
80.0%
Dash Punctuation20
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
131
38.8%
022
27.5%
221
26.2%
56
 
7.5%
Dash Punctuation
ValueCountFrequency (%)
-20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
131
31.0%
022
22.0%
221
21.0%
-20
20.0%
56
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131
31.0%
022
22.0%
221
21.0%
-20
20.0%
56
 
6.0%

columna_53
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_54
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_55
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean690.7
Minimum298
Maximum1181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:34.090574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum298
5-th percentile312.85
Q1367.25
median660
Q31027.75
95-th percentile1153.1
Maximum1181
Range883
Interquartile range (IQR)660.5

Descriptive statistics

Standard deviation352.757093
Coefficient of variation (CV)0.5107240379
Kurtosis-1.789189644
Mean690.7
Median Absolute Deviation (MAD)311.5
Skewness0.2815221796
Sum6907
Variance124437.5667
MonotonicityNot monotonic
2022-07-27T10:49:34.215120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3311
10.0%
7631
10.0%
8261
10.0%
3711
10.0%
2981
10.0%
11191
10.0%
3661
10.0%
10951
10.0%
5571
10.0%
11811
10.0%
ValueCountFrequency (%)
2981
10.0%
3311
10.0%
3661
10.0%
3711
10.0%
5571
10.0%
7631
10.0%
8261
10.0%
10951
10.0%
11191
10.0%
11811
10.0%
ValueCountFrequency (%)
11811
10.0%
11191
10.0%
10951
10.0%
8261
10.0%
7631
10.0%
5571
10.0%
3711
10.0%
3661
10.0%
3311
10.0%
2981
10.0%

columna_56
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246.3
Minimum217
Maximum268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:34.329801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum217
5-th percentile223.75
Q1237
median239
Q3263
95-th percentile268
Maximum268
Range51
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.79544261
Coefficient of variation (CV)0.07225108653
Kurtosis-1.290044839
Mean246.3
Median Absolute Deviation (MAD)14.5
Skewness-0.02189425218
Sum2463
Variance316.6777778
MonotonicityNot monotonic
2022-07-27T10:49:34.427972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2373
30.0%
2632
20.0%
2682
20.0%
2171
 
10.0%
2411
 
10.0%
2321
 
10.0%
ValueCountFrequency (%)
2171
 
10.0%
2321
 
10.0%
2373
30.0%
2411
 
10.0%
2632
20.0%
2682
20.0%
ValueCountFrequency (%)
2682
20.0%
2632
20.0%
2411
 
10.0%
2373
30.0%
2321
 
10.0%
2171
 
10.0%

columna_57
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_58
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_59
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

columna_60
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
1
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
110
100.0%

Length

2022-07-27T10:49:34.538907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:34.631078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
110
100.0%

Most occurring characters

ValueCountFrequency (%)
110
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common10
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
110
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110
100.0%

columna_61
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583618.2
Minimum16848
Maximum2804762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:34.722149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16848
5-th percentile23538.6
Q1116040
median286571
Q3662148
95-th percentile1918661.6
Maximum2804762
Range2787914
Interquartile range (IQR)546108

Descriptive statistics

Standard deviation830887.6113
Coefficient of variation (CV)1.423683517
Kurtosis6.943500111
Mean583618.2
Median Absolute Deviation (MAD)262289
Skewness2.515321397
Sum5836182
Variance6.903742227 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:34.825800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2895701
10.0%
6732241
10.0%
1758001
10.0%
168481
10.0%
6289201
10.0%
28047621
10.0%
2835721
10.0%
961201
10.0%
8356501
10.0%
317161
10.0%
ValueCountFrequency (%)
168481
10.0%
317161
10.0%
961201
10.0%
1758001
10.0%
2835721
10.0%
2895701
10.0%
6289201
10.0%
6732241
10.0%
8356501
10.0%
28047621
10.0%
ValueCountFrequency (%)
28047621
10.0%
8356501
10.0%
6732241
10.0%
6289201
10.0%
2895701
10.0%
2835721
10.0%
1758001
10.0%
961201
10.0%
317161
10.0%
168481
10.0%

columna_62
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1382697.992
Minimum0
Maximum6165792.447
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:34.926576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1137091.7205
median648458.064
Q31853943.122
95-th percentile4446297.174
Maximum6165792.447
Range6165792.447
Interquartile range (IQR)1716851.401

Descriptive statistics

Standard deviation1887598.106
Coefficient of variation (CV)1.365155744
Kurtosis4.887829736
Mean1382697.992
Median Absolute Deviation (MAD)648458.064
Skewness2.081212943
Sum13826979.92
Variance3.563026609 × 1012
MonotonicityNot monotonic
2022-07-27T10:49:35.010559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
02
20.0%
807900.31
10.0%
1878294.961
10.0%
489015.8281
10.0%
1780887.6071
10.0%
6165792.4471
10.0%
266411.95921
10.0%
2344691.8391
10.0%
93984.974281
10.0%
ValueCountFrequency (%)
02
20.0%
93984.974281
10.0%
266411.95921
10.0%
489015.8281
10.0%
807900.31
10.0%
1780887.6071
10.0%
1878294.961
10.0%
2344691.8391
10.0%
6165792.4471
10.0%
ValueCountFrequency (%)
6165792.4471
10.0%
2344691.8391
10.0%
1878294.961
10.0%
1780887.6071
10.0%
807900.31
10.0%
489015.8281
10.0%
266411.95921
10.0%
93984.974281
10.0%
02
20.0%

columna_63
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.193247
Minimum0
Maximum2.96333
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:35.110191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.3416625
median2.78583
Q32.8018725
95-th percentile2.9040785
Maximum2.96333
Range2.96333
Interquartile range (IQR)0.46021

Descriptive statistics

Standard deviation1.173293906
Coefficient of variation (CV)0.534957488
Kurtosis1.122324756
Mean2.193247
Median Absolute Deviation (MAD)0.032915
Skewness-1.662309939
Sum21.93247
Variance1.376618589
MonotonicityNot monotonic
2022-07-27T10:49:35.198177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.792
20.0%
02
20.0%
2.781661
10.0%
2.831661
10.0%
2.198331
10.0%
2.771661
10.0%
2.805831
10.0%
2.963331
10.0%
ValueCountFrequency (%)
02
20.0%
2.198331
10.0%
2.771661
10.0%
2.781661
10.0%
2.792
20.0%
2.805831
10.0%
2.831661
10.0%
2.963331
10.0%
ValueCountFrequency (%)
2.963331
10.0%
2.831661
10.0%
2.805831
10.0%
2.792
20.0%
2.781661
10.0%
2.771661
10.0%
2.198331
10.0%
02
20.0%

columna_64
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48229.2
Minimum0
Maximum183545
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:35.317451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15014.25
median21957.5
Q380547
95-th percentile139512.5
Maximum183545
Range183545
Interquartile range (IQR)75532.75

Descriptive statistics

Standard deviation58875.67459
Coefficient of variation (CV)1.220747485
Kurtosis2.162133087
Mean48229.2
Median Absolute Deviation (MAD)21957.5
Skewness1.473105151
Sum482292
Variance3466345059
MonotonicityNot monotonic
2022-07-27T10:49:35.418714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
02
20.0%
325501
10.0%
835141
10.0%
109371
10.0%
716461
10.0%
1835451
10.0%
113651
10.0%
856951
10.0%
30401
10.0%
ValueCountFrequency (%)
02
20.0%
30401
10.0%
109371
10.0%
113651
10.0%
325501
10.0%
716461
10.0%
835141
10.0%
856951
10.0%
1835451
10.0%
ValueCountFrequency (%)
1835451
10.0%
856951
10.0%
835141
10.0%
716461
10.0%
325501
10.0%
113651
10.0%
109371
10.0%
30401
10.0%
02
20.0%

columna_65
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size208.0 B
0
28173
20804
61168
44796

Length

Max length5
Median length1
Mean length2.6
Min length1

Characters and Unicode

Total characters26
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)40.0%

Sample

1st row28173
2nd row0
3rd row20804
4th row0
5th row61168

Common Values

ValueCountFrequency (%)
06
60.0%
281731
 
10.0%
208041
 
10.0%
611681
 
10.0%
447961
 
10.0%

Length

2022-07-27T10:49:35.533129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T10:49:35.657174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
06
60.0%
281731
 
10.0%
208041
 
10.0%
611681
 
10.0%
447961
 
10.0%

Most occurring characters

ValueCountFrequency (%)
08
30.8%
83
 
11.5%
13
 
11.5%
43
 
11.5%
63
 
11.5%
22
 
7.7%
72
 
7.7%
31
 
3.8%
91
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08
30.8%
83
 
11.5%
13
 
11.5%
43
 
11.5%
63
 
11.5%
22
 
7.7%
72
 
7.7%
31
 
3.8%
91
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08
30.8%
83
 
11.5%
13
 
11.5%
43
 
11.5%
63
 
11.5%
22
 
7.7%
72
 
7.7%
31
 
3.8%
91
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08
30.8%
83
 
11.5%
13
 
11.5%
43
 
11.5%
63
 
11.5%
22
 
7.7%
72
 
7.7%
31
 
3.8%
91
 
3.8%

columna_66
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331200.3
Minimum8895
Maximum1319527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:35.745719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8895
5-th percentile11151.75
Q122370
median229522.5
Q3459906
95-th percentile972582.85
Maximum1319527
Range1310632
Interquartile range (IQR)437536

Descriptive statistics

Standard deviation402939.7884
Coefficient of variation (CV)1.216604539
Kurtosis3.896000773
Mean331200.3
Median Absolute Deviation (MAD)215598
Skewness1.818284702
Sum3312003
Variance1.623604731 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:35.855169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1991061
10.0%
4695701
10.0%
476631
10.0%
139391
10.0%
4309141
10.0%
13195271
10.0%
2599391
10.0%
88951
10.0%
5485401
10.0%
139101
10.0%
ValueCountFrequency (%)
88951
10.0%
139101
10.0%
139391
10.0%
476631
10.0%
1991061
10.0%
2599391
10.0%
4309141
10.0%
4695701
10.0%
5485401
10.0%
13195271
10.0%
ValueCountFrequency (%)
13195271
10.0%
5485401
10.0%
4695701
10.0%
4309141
10.0%
2599391
10.0%
1991061
10.0%
476631
10.0%
139391
10.0%
139101
10.0%
88951
10.0%

columna_67
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346694.4
Minimum13910
Maximum1319527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:35.984908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13910
5-th percentile13923.05
Q157385
median243609
Q3486454
95-th percentile972582.85
Maximum1319527
Range1305617
Interquartile range (IQR)429069

Descriptive statistics

Standard deviation398594.8249
Coefficient of variation (CV)1.149700788
Kurtosis3.755143954
Mean346694.4
Median Absolute Deviation (MAD)227815.5
Skewness1.778873641
Sum3466944
Variance1.588778344 × 1011
MonotonicityNot monotonic
2022-07-27T10:49:36.101782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2272791
10.0%
4695701
10.0%
684671
10.0%
139391
10.0%
4920821
10.0%
13195271
10.0%
2599391
10.0%
536911
10.0%
5485401
10.0%
139101
10.0%
ValueCountFrequency (%)
139101
10.0%
139391
10.0%
536911
10.0%
684671
10.0%
2272791
10.0%
2599391
10.0%
4695701
10.0%
4920821
10.0%
5485401
10.0%
13195271
10.0%
ValueCountFrequency (%)
13195271
10.0%
5485401
10.0%
4920821
10.0%
4695701
10.0%
2599391
10.0%
2272791
10.0%
684671
10.0%
536911
10.0%
139391
10.0%
139101
10.0%

columna_68
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48229.2
Minimum0
Maximum183545
Zeros2
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size208.0 B
2022-07-27T10:49:36.205070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15014.25
median21957.5
Q380547
95-th percentile139512.5
Maximum183545
Range183545
Interquartile range (IQR)75532.75

Descriptive statistics

Standard deviation58875.67459
Coefficient of variation (CV)1.220747485
Kurtosis2.162133087
Mean48229.2
Median Absolute Deviation (MAD)21957.5
Skewness1.473105151
Sum482292
Variance3466345059
MonotonicityNot monotonic
2022-07-27T10:49:36.306693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
02
20.0%
325501
10.0%
835141
10.0%
109371
10.0%
716461
10.0%
1835451
10.0%
113651
10.0%
856951
10.0%
30401
10.0%
ValueCountFrequency (%)
02
20.0%
30401
10.0%
109371
10.0%
113651
10.0%
325501
10.0%
716461
10.0%
835141
10.0%
856951
10.0%
1835451
10.0%
ValueCountFrequency (%)
1835451
10.0%
856951
10.0%
835141
10.0%
716461
10.0%
325501
10.0%
113651
10.0%
109371
10.0%
30401
10.0%
02
20.0%

columna_69
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing10
Missing (%)100.0%
Memory size208.0 B

Interactions

2022-07-27T10:49:19.698380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:41.316209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:44.839780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:47.847707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:51.020070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:54.006392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:57.411833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:00.875040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:03.943438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:07.047745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:10.369719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:13.555816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:16.774763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:19.817151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:23.158620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:26.100685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:29.178089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:32.307217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:35.671576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:38.970434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:41.942649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:44.935825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:47.861282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:50.931647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:54.034733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:56.997027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:00.034108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:03.558730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:06.858014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:10.121876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:13.380356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:16.460111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:19.816115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:41.498215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:44.937800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:47.949554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:51.126461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:54.218404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:57.511862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:00.994148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:04.046537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:07.133870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:10.459200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:13.668749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:16.874232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:19.911170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:23.261377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:26.196691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:29.280430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:32.410038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:35.788536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:39.085035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:42.038418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:45.032827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:47.975813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:51.052896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:54.131184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:57.126545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:00.178911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:03.665517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:06.958101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:10.223476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:13.476708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:16.563185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:19.966053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:41.596725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:45.043310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:48.033885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:51.219082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:54.309877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:57.609131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:01.097299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:04.121773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:07.317375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:10.538491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:13.750650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:16.957391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:19.988721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:23.352427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:26.269585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:29.369466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:32.505821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:35.915437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:39.164294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:42.121179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:45.118711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:48.048465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:51.148042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:54.240684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:57.202329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:00.291534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:03.769230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:07.047235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:10.321560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:13.559411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:16.654210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:20.080912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:41.703793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:45.120283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:48.115850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:51.300889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:54.407395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:57.712883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:01.183545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:04.221900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:07.424583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:10.626206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:13.845372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:17.101019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:20.200550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:23.428467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:26.384074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:29.459442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:32.600152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:36.007141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:39.254269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:42.197163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:45.202074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:48.237308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:51.240837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:54.312077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:57.280854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:00.421823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:03.870281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:07.157317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:10.406958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:13.652436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:16.768455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:20.206264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:41.812209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:45.204979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:48.212862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:51.387085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:54.509464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:57.813899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:01.284412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:04.302648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:07.514170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:10.721278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:13.925852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:17.186294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:20.289089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:23.508378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:26.474227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:29.585799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T10:48:28.872455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:32.041157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:35.268186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:38.690929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:41.674655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:44.649131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:47.599735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:50.571077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:53.773566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:56.688734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:59.779607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:03.270212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:06.475155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:09.772455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:13.044237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:16.187008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:19.318502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:22.979118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:44.616422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:47.674917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:50.819237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:53.811104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:57.222629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:00.631272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:03.764477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:06.872599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:10.144494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:13.360395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:16.595599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:19.641175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:22.965506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:25.932872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:28.970720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:32.126912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:35.479481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:38.774991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:41.772587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:44.736807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:47.678131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:50.762398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:53.855864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:56.801924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:59.856540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:03.356088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:06.658375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:09.929104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:13.160745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:16.275837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:19.411451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:23.074941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:44.734176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:47.760643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:50.909833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:53.904033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:47:57.317745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:00.759716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:03.864539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:06.963264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:10.245905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:13.460535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:16.681197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:19.724557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:23.060638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:26.022225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:29.085265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:32.210188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:35.564720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:38.873347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:41.858684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:44.821399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:47.765703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:50.844452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:53.949962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:56.904453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:48:59.934715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:03.463084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:06.764895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:10.027265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:13.273711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:16.367945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T10:49:19.509960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-27T10:49:36.604049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-27T10:49:37.397791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-27T10:49:38.210685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-27T10:49:38.962642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-27T10:49:39.261934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-27T10:49:23.463187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-27T10:49:24.080045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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042000561941742970881008217751572022-06-301289570102895724296046610199106281732272793255003255000000NaN00926061312895782316561289572.790002021-05-112021-05-104632021-09-302021-08-0824141463C4331008217751571008217751572022-07-05NaN2.- CoasNaNCompra Cuotas EmisorNaN52429602021-11-05NaNNaN331237NaNNaNNaN12895708.079003e+052.790003255028173199106227279.032550.0NaN
140029105551706770801008217842112022-06-301673224242805146968020354446957004695708351408351400000NaN00925245912805123645173002.790002021-10-112021-10-104632022-01-052021-08-0824101463C4331008217842111008217842112023-09-10NaN2.- CoasNaNCompra Cuotas EmisorNaN54696802021-11-10NaNNaN763263NaNNaNNaN16732241.878295e+062.79000835140469570469570.083514.0NaN
24002852021178847600106203559382022-06-301175800247325115906598944766320804684671093701093700000NaN002115382513952258586003219752.781662021-05-112020-05-102602021-11-102020-01-0624101260C25210620355938106203559382022-09-05NaN7.- RefinanciamientoNaNRENEGOCIACION X X REFINANCIAMIENTO TOTALNaN51159062021-11-05NaNNaN826237NaNNaNNaN11758004.890158e+052.7816610937208044766368467.010937.0NaN
34003681769173567839107212768202022-06-301168421214041684201393901393900000000NaN001115444228081014040000.000002021-05-102021-05-084632021-07-092021-06-2924101463C43210721276820107212768202022-07-05NaN2.- CoasNaNCompra Cuotas Comercio TRANSBANKNaN5168422021-10-05NaNNaN371268NaNNaNNaN1168480.000000e+000.00000001393913939.00.0NaN
440034377741356526521009211919982022-06-301628920106289254277286148430914611684920827164607164600000NaN00956602816289285031361628922.831662021-05-112021-05-1012021-07-102021-10-09241011C2521009211919981009211919982022-07-05NaN7.- RefinanciamientoNaNRENEGOCIACION X X REFINANCIAMIENTO TOTALNaN55427722021-11-05NaNNaN298237NaNNaNNaN16289201.780888e+062.831667164661168430914492082.071646.0NaN
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Last rows

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